Using Theia Markerless Motion capture to measure the impact of adjusting deep brain stimulation parameters on gait in a Parkinson’s Disease case study
Bibliographic record
Abstract
The way one swings their arms, turns their torso, and moves their feet, are gait parameters that are commonly disturbed in patients with Parkinson’s Disease (PD).1 Deep brain stimulation in the subthalamic nucleus (STN-DBS) has proven to be an effective long-term therapy for reducing cardinal motor symptoms and improving quality of life, compared to standard medical treatments like levodopa-based therapies.2-4 However, optimizing STN-DBS settings for individual needs can be a complex process, as standard clinical tools for PD progression, such as the Unified Parkinson's Disease Rating Scale, may be subjective and have limitations.5,6 This project aimed to explore markerless motion capture as a novel, objective measurement of gait progression in PD. The study used an n=1 case design to investigate how gait metrics change with adjustments to DBS settings. The participant, a PD patient with STN-DBS, underwent five randomized, double-blind trials in which their DBS frequency (Hz) and current strength (mA) were adjusted. The following combinations of left and right ventral electrode settings were used: Left: 179 Hz, 3.1 mA; Right: 179 Hz, 2.9mA (Baseline) Left: 149 Hz, 3.1 mA; Right: 149 Hz, 2.9mA (149 low) Left: 149 Hz, 3.4 mA; Right: 149 Hz, 3.2 mA (149 high) Left: 104 Hz, 3.1 mA; Right: 104 Hz, 2.9mA (104 low) Left: 104 Hz, 3.3 mA; Right: 104 Hz, 3.2 mA (104 high) During each 5-minute trial, the participant walked on a treadmill at a comfortable speed while being recorded using a Theia3D markerless motion capture system. The recordings were analyzed using Visual3D software, which quantified gait metrics including cadence, step length, stride length, posture, and arm swing. Notably, differences in the participant’s arm swing between the left and right sides were observed across the various DBS settings. This technology provided objective, measurable data on gait, offering a potential tool for future gait assessments in PD. Its applicability extends to long-term tracking of patient progress, with the potential for markerless motion capture to transform how clinicians evaluate the efficacy of DBS in patients with PD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".